“Precision farming” is a broad term that embraces practices such as:    field yield mapping aimed at accurately mapping the crop-producing productivity of a field so that:            seeds and agro-chemicals can be economically and correctly applied without over- and under-dosing the plants;        harvesting and other agricultural machinery can be adjusted to take account of varying crop conditions from place to place in a field;            modelling, of process conditions, within agricultural machines, and developing control philosophies aimed at:            improving machine efficiency or workrate;        reducing fuel consumption; and        improving the quality of work carried out by agricultural machines; and            providing alerts and reports of abnormal conditions in crops.
Sometimes the overall improvement in farming efficiency resulting from an individual precision farming practice might be only a few percent; but cumulatively such efforts have dramatically improved productivity in mechanised farming over recent years.
The significance of harvesters, such as combine harvesters, to precision farming derives principally from the following factors:    (i) A harvester is able to assess the output of a farming operation for example in terms of crop yield. A field map of such data is invaluable in improving farming efficiency in future crop growing and harvesting seasons; and    (ii) Harvesters are complicated machines that provide numerous sites for the location of transducers whose function is to gather data on the harvesting processes and the crops passing through the machine.
As noted, the combine harvester has been particularly useful in producing maps indicating the expected crop yield at different locations in a field. Farmers can use such maps (that are readily stored in digital form in a computer memory) to control in an accurate way the processes forming part of a crop growing season, so that the yield of the field is maximized.
Prior art techniques for yield mapping, however, are limited primarily because they concentrate on the quantity of the useable part of the crop that is conveyed to e.g. the clean grain tank in a combine harvester.
Although measurements of e.g. the mass flowrate of clean grain to the clean grain tank may readily be compensated for some variables such as grain moisture content and grain type, some difficulties remain.
Significant among these is the fact that mass flowrate measurements of crop yield generally take no account of crop losses arising from e.g. incomplete or faulty threshing of ears in the threshing drum or another part of the harvester where grain separation occurs. Where the machine load exceeds the threshing, separation and/or cleaning capacity of the machine a portion of the harvested grain will not be separated from the straw and chaff and be deposited therewith on the field behind the machine.
It is known, for example, that the extent to which (or the ease with which) ears are threshed in the threshing drum of a combine harvester is strongly dependent on the feedrate of crop into the harvesting machine, when such factors as grain and straw moisture, crop variety and straw length are kept as constants. Thus for higher feedrates relatively less grain is separated in the first concave threshing drum section than in the case of low feedrates. More grain has to be separated in the further stages of the harvester.
Consequently for such high feedrates a lesser proportion of the crop is therefore likely to reach the clean grain tank, with the result that a yield measurement taken at such a location may be inaccurate. Furthermore such a measurement takes no account of the extent to which grains become damaged or lost within the combine harvester.
In reality a great number of variables influences the extent to which the threshing and separating sections are able to separate grain from other plant matter such as chaff and straw. Such variables include, but are not limited to:    the nature of the soil in which the crop grows;    settings of various adjustable components of the harvesting machine, e.g. the height of the header bar in a combine harvester, which directly influences the straw to grain ratio;    the slope of the field in which the machine operates;    the moisture content of the crop;    the crop type;    the forward speed of the harvesting machine;    the presence of weed patches;    the state (wear) of the machine elements;    the type of installed machine elements, e.g. the type of rasp bars; and so on.
According to a first aspect of the invention there is provided a method of substantially continuously optimizing a stochastic parameter  that characterizes the instantaneously prevailing readiness with which crop is processed in a harvesting machine, including the step of recursively calculating the optimized parameter value in accordance with the following algorithm:(t)=f((t−1),ε(t,(t−1)))  (A)wherein:                (t) is the optimized stochastic parameter value at time t; and        ε(t,(t)) is an error prediction function.        
Such a method is highly suited to the continuous optimization of the highly stochastic parameter  that, when applied to the threshing and separation process a combine harvester, may fairly be termed a “threshability” parameter, i.e. an indication of the extent to which the harvesting machine is capable of threshing the crop at time t.
Such a parameter is useable in various ways, as discussed hereinbelow.
The algorithm generally may take the form of:(t)=f((t−1), . . . ,(t−n),ε(t), . . . ,ε(t−nε),t).
The method of the broad aspect of the invention can readily be carried out using a suitably programmed computer carried by or forming part of the harvesting machine.
Preferably the algorithm (A) has the form:(t)=(t−1)+γ(t)r−1(t)ψ(t,(t−1))ε(t,(t−1))wherein                γ(t) is a gain term;        r(t) is a scalar approximation of a Hessian V″ () in which V is indicative which V is a quadratic error criterion;        
            ψ      ⁡              (                  t          ,          ϑ                )              =                  ⅆ                              y            ^                    ⁡                      (                          t              ,              ϑ                        )                                      ⅆ        ϑ              ,in which ŷ(t,) is an estimation of a value indicative of the effectiveness of said crop processing in said harvesting machine said estimation being based on stochastic parameter ; and                ε(t,(t−1)) is the difference between the actual effectiveness value y(t) and the estimated value ŷ(t,) based on the previously optimized parameter (t−1).        
Preferably the algorithm (A) includes an estimation of r(t) that is weighted to reduce the influence, on the optimized parameter values , of past measurements.
This aspect of the method renders the parameter optimization more realistic and robust for a wide range of working conditions.
The parameter  may be usable in a model for the relation between a value u(t) indicative of the feedrate of crop into the harvesting machine and a value y(t) indicative of the effectiveness of an operation processing said crop in said harvesting machine. The estimated value ŷ(t,) is then an estimation of the effectiveness obtained by the application of said model to the feedrate values u(t).
In this manner the model can be updated continuously in order to meet any changes to the process caused by a wide range of changing conditions, e.g. varying crop properties such as ripeness or moisture or changes in inclination of the machine.
Advantageously, the model may comprise an exponential function.
Such form provides some computational advantages for the optimization of .
The effectiveness may take the form of a value indicative of crop flow, e.g. crop losses at the end of the separation or the cleaning section. It may also comprise the crop flow in a return system.
According to a second aspect of the invention there is provided a method of operating a harvesting machine comprising the steps of:
substantially continuously optimizing a stochastic parameter that characterizes the instantaneously prevailing readiness with which the harvesting machine processes crop; and
substantially continuously adjusting a performance variable of the harvesting machine in dependence on the instantaneous, optimized value of said parameter in order to optimize the load of the harvesting machine so as to keep a value indicative of the effectiveness of said harvesting machine below a predetermined value.
Such effectiveness value may comprise the losses of useable crop parts such as separation or cleaning sieve losses, or a proportion of damaged useable crop parts, e.g. broken grain kernels, or a proportion of unwanted material in the useable crop parts, e.g. chaff and straw and particles in the clean grain.
Optimizing the machine load may comprise optimizing the feedrate of crop into the harvesting machine, e.g. by adapting the travel speed of the harvesting machine.
Conveniently the step of adjusting a performance variable of the harvesting machine occurs in dependence on the output of an inverted form of an effectiveness estimation function:ŷ(t,)=exp(u(t))−1.  (B)Herein u(t) may be the measured feedrate and ŷ(t,) the grain losses.
According to a third aspect of the invention there is provided a method of mapping one or more field lots for variations in a stochastic parameter that characterizes the instantaneously prevailing readiness with which a harvesting machine processes crop, the method comprising the steps of:
operating a harvesting machine to harvest crop in a said field lot;
simultaneously measuring the machine load and the machine effectiveness and determining the position of the machine in the field lot;
storing data indicative of the position of the harvesting machine at time t;
using the measured machine load and machine effectiveness data in an optimization of said parameter; and
mapping the optimized parameter values obtained from the using the measured machine load and machine effectiveness data step so as to produce a parameter map of the field lot.
According to a fourth aspect of the invention there is provided a method of operating a harvesting machine comprising the steps of:
substantially continuously optimizing a stochastic parameter that characterizes the instantaneously prevailing readiness with which the harvesting machine separates useable crop parts from other plant matter; and
sending a display signal, that is indicative of the instantaneous parameter value, to a display device.
Preferably the display signal indicates an abnormal parameter value.
Preferably in each of the second, third and fourth aspects of the invention the optimization step is in accordance with the first aspect of the invention. Thus the method of the first aspect of the invention is highly versatile in its application.
Conveniently in each of the 2nd to 4th aspects of the invention, when the parameter optimization is according to the first aspect of the invention, said selected part of the harvesting machine is selected from:
the separation section, e.g. the straw walkers or a rotary separator;
the sieve;
the return flow system;
the cleaning section; or
the grain elevator;
of a combine harvester.